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» On the Complexity of Approximating the VC Dimension
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STOC
2006
ACM
244views Algorithms» more  STOC 2006»
15 years 9 months ago
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
We introduce a new low-distortion embedding of d 2 into O(log n) p (p = 1, 2), called the Fast-Johnson-LindenstraussTransform. The FJLT is faster than standard random projections ...
Nir Ailon, Bernard Chazelle
ICML
2004
IEEE
15 years 10 months ago
SVM-based generalized multiple-instance learning via approximate box counting
The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based imageretrieval. Recently, a generalization of thi...
Qingping Tao, Stephen D. Scott, N. V. Vinodchandra...
JSCIC
2007
89views more  JSCIC 2007»
14 years 9 months ago
Adjoint Recovery of Superconvergent Linear Functionals from Galerkin Approximations. The One-dimensional Case
In this paper, we extend the adjoint error correction of Pierce and Giles [SIAM Review, 42 (2000), pp. 247-264] for obtaining superconvergent approximations of functionals to Gale...
Bernardo Cockburn, Ryuhei Ichikawa
ISCI
2000
84views more  ISCI 2000»
14 years 9 months ago
Automating the approximate record-matching process
Data Quality has many dimensions one of which is accuracy. Accuracy is usually compromised by errors accidentally or intensionally introduced in a database system. These errors re...
Vassilios S. Verykios, Ahmed K. Elmagarmid, Elias ...
ALT
2006
Springer
15 years 6 months ago
How Many Query Superpositions Are Needed to Learn?
Abstract. This paper introduces a framework for quantum exact learning via queries, the so-called quantum protocol. It is shown that usual protocols in the classical learning setti...
Jorge Castro